War Deaths by Nasir Sharaf

Steven Pinker has argued in his best selling book “the Better Angels of Our Nature” that human violence has been trending downward. I wanted to validate his claims, so I used one of his datasets - the Uppsala Conflict Data Program Battle related Deaths Dataset. The exploration is as follows:

##   conflict_id dyad_id year
## 1       13349   14273 2013
## 2       13349   14273 2014
## 3       13349   14273 2015
## 4       13721   14847 2015
## 5       13692   11199 2001
## 6       13641   14669 2016
##                                            location_inc
## 1                                       Myanmar (Burma)
## 2                                       Myanmar (Burma)
## 3                                       Myanmar (Burma)
## 4                                               Algeria
## 5 Afghanistan, United Kingdom, United States of America
## 6                                               Nigeria
##                          side_a
## 1 Government of Myanmar (Burma)
## 2 Government of Myanmar (Burma)
## 3 Government of Myanmar (Burma)
## 4         Government of Algeria
## 5     Government of Afghanistan
## 6         Government of Nigeria
##                                                        side_a_2nd
## 1                                                                
## 2                                                                
## 3                                                                
## 4                                                                
## 5                                                                
## 6 Government of Cameroon, Government of Chad, Government of Niger
##                                                                 side_b
## 1                                                                 PSLF
## 2                                                                 PSLF
## 3                                                                 PSLF
## 4                                                                   IS
## 5 Government of United Kingdom, Government of United States of America
## 6                                                                   IS
##   side_b_id sideb2nd incompatibility territory_name bd_best bd_low bd_high
## 1      5965                        1        Palaung      29     29      30
## 2      5965                        1        Palaung      37     37     203
## 3      5965                        1        Palaung     106    106     181
## 4       234                        1  Islamic State      26     26      26
## 5     28, 3                        2                   1397   1388    2075
## 6       234                        1  Islamic State    2213   2210    2311
##   type_of_conflict battle_location gwno_a    gwno_a_2nd gwno_b gwno_b_2nd
## 1                3 Myanmar (Burma)    775                                
## 2                3 Myanmar (Burma)    775                                
## 3                3 Myanmar (Burma)    775                                
## 4                3         Algeria    615                                
## 5                2     Afghanistan    700               200, 2           
## 6                4         Nigeria    475 471, 483, 436                  
##      gwno_loc gwno_battle  region version
## 1         775         775       3      17
## 2         775         775       3      17
## 3         775         775       3      17
## 4         615         615       4      17
## 5 2, 200, 700         700 1, 3, 5      17
## 6         475         475       4      17

Univariate Plots Section

##  [1] "conflict_id"      "dyad_id"          "year"            
##  [4] "location_inc"     "side_a"           "side_a_2nd"      
##  [7] "side_b"           "side_b_id"        "sideb2nd"        
## [10] "incompatibility"  "territory_name"   "bd_best"         
## [13] "bd_low"           "bd_high"          "type_of_conflict"
## [16] "battle_location"  "gwno_a"           "gwno_a_2nd"      
## [19] "gwno_b"           "gwno_b_2nd"       "gwno_loc"        
## [22] "gwno_battle"      "region"           "version"

I first just wanted to look at the dataset and see what variables we have to work with. Some of the variables are confusing, so I consulted the codebook to better understand that dataset.

There were two datasets, one that was a “Dyadic” set, a collection of two-party battles, and the set I used which was a conflict set. The Dyadic ID was carried over from the dyadic set.

Incompatibility returns 1 or 2. 1 stands for ‘incompatibility about government’ indicating that the source of the conflict is a discrepency over governmental affairs. 2 stands for ‘incompatibility about territory’ indicating a resource based conflict.

Another prominent variable will be the type_of_conflict variable. It is also given a numeric evaluation from 1 to 4. 1 stands for extrasystemic - I will be honest, the codebook is lacking for explanatinon as to what this could mean. 2 stands for interstate, meaning two or more countries at war with each other. 3 stands for internal, so mostly civil war or revolutions that do not involve other state entities. 4 stands for international internal, which would refer to a conflict in which a state is deal with internal unrest but has also accepted support from foreign troops.

The first real plot was to set battle deaths against the years they occured. I then filtered by type of conflict, to see if battle deaths were overrepresented by one type of conflict. I could see that battle deaths by raw count were far, far more prevalent in internal conflicts (type 3) but internationalized internal conflicts (type 4), that is internal conflicts in which foreign troops were present, were rising in recent years.

Type of conflict tells us who the main conflict is with, but not what the conflict is about. I thought it would be best to see battle deaths by incompatibility. The codebook tells us that there is only two categories: 1 stands for ‘incompatibility about government’ indicating that the source of the conflict is a discrepency over governmental affairs. 2 stands for ‘incompatibility about territory’ indicating a resource based conflict. However, there was a “3” that was plotted, so this had to be removed. We can see that there seems to be an even split between type 1 incompatibility and type 2 incompatibility.

Type of conflict also doesn’t tell us where the conflict is, so the next area to explore was the region of battle deaths. There was a similar moment of data cleaning to clean out some battles that spanned a few regions. We can see that region three (asia) and region four (africa) had the highest battle deaths. Region two (the middle east) seems to be trending up while region five (the Americas) is trending down.

## bd$battle_location: Afghanistan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      30    1975    3512    3651    5189    8937 
## -------------------------------------------------------- 
## bd$battle_location: Afghanistan, Afghanistan, Pakistan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    4734    5732    6705    6522    7326    8048 
## -------------------------------------------------------- 
## bd$battle_location: Afghanistan, India
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     281     281     281     281     281     281 
## -------------------------------------------------------- 
## bd$battle_location: Afghanistan, Pakistan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    54.0   752.8  2276.0  5227.4  7506.8 16581.0 
## -------------------------------------------------------- 
## bd$battle_location: Afghanistan, Pakistan, Saudi Arabia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     108     177     246     244     312     378 
## -------------------------------------------------------- 
## bd$battle_location: Afghanistan, Pakistan, Saudi Arabia, Somalia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     174     174     174     174     174     174 
## -------------------------------------------------------- 
## bd$battle_location: Afghanistan, Pakistan, Somalia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    38.0   161.8   214.0   229.0   281.2   450.0 
## -------------------------------------------------------- 
## bd$battle_location: Afghanistan, Pakistan, Somalia, Yemen (North Yemen)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     290     290     290     290     290     290 
## -------------------------------------------------------- 
## bd$battle_location: Afghanistan, Pakistan, Syria
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    65.0    74.5    84.0    84.0    93.5   103.0 
## -------------------------------------------------------- 
## bd$battle_location: Afghanistan, Pakistan, United States of America
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1583    1583    1583    1583    1583    1583 
## -------------------------------------------------------- 
## bd$battle_location: Afghanistan, Tajikistan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   368.0   624.8   881.5   881.5  1138.2  1395.0 
## -------------------------------------------------------- 
## bd$battle_location: Albania, Serbia (Yugoslavia)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1404    1404    1404    1404    1404    1404 
## -------------------------------------------------------- 
## bd$battle_location: Algeria
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    26.0   236.0   472.0   724.3   931.0  3029.0 
## -------------------------------------------------------- 
## bd$battle_location: Algeria, Chad, Niger
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     451     451     451     451     451     451 
## -------------------------------------------------------- 
## bd$battle_location: Algeria, Mali
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     229     229     229     229     229     229 
## -------------------------------------------------------- 
## bd$battle_location: Angola
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      25      37      72    1350    1123   12054 
## -------------------------------------------------------- 
## bd$battle_location: Angola, DR Congo (Zaire)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    3924    3924    3924    3924    3924    3924 
## -------------------------------------------------------- 
## bd$battle_location: Angola, Namibia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   461.0   730.5  1000.0  1244.7  1636.5  2273.0 
## -------------------------------------------------------- 
## bd$battle_location: Argentina, Israel, Lebanon
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      87      87      87      87      87      87 
## -------------------------------------------------------- 
## bd$battle_location: Armenia, Azerbaijan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    25.0    48.5   141.0   729.7  1339.0  2167.0 
## -------------------------------------------------------- 
## bd$battle_location: Azerbaijan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   29.00   32.00   35.00   40.14   45.00   63.00 
## -------------------------------------------------------- 
## bd$battle_location: Bangladesh
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   25.00   29.75   43.00   42.67   54.00   62.00 
## -------------------------------------------------------- 
## bd$battle_location: Belgium, Iraq, Libya, Syria
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   11846   11846   11846   11846   11846   11846 
## -------------------------------------------------------- 
## bd$battle_location: Bhutan, India
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      34      34      34      34      34      34 
## -------------------------------------------------------- 
## bd$battle_location: Bosnia-Herzegovina
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     133     526    1197    1332    2053    2835 
## -------------------------------------------------------- 
## bd$battle_location: Bosnia-Herzegovina, Bosnia-Herzegovina, Croatia, Serbia (Yugoslavia)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    4018    4018    4018    4018    4018    4018 
## -------------------------------------------------------- 
## bd$battle_location: Bosnia-Herzegovina, Croatia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   116.0   301.5   487.0   487.0   672.5   858.0 
## -------------------------------------------------------- 
## bd$battle_location: Bosnia-Herzegovina, Croatia, Croatia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     126     126     126     126     126     126 
## -------------------------------------------------------- 
## bd$battle_location: Burundi
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    49.0   135.0   279.0   428.7   598.5  1348.0 
## -------------------------------------------------------- 
## bd$battle_location: Burundi, Burundi, DR Congo (Zaire)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1114    1114    1114    1114    1114    1114 
## -------------------------------------------------------- 
## bd$battle_location: Cambodia (Kampuchea)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   118.0   247.0   274.0   404.6   579.0   788.0 
## -------------------------------------------------------- 
## bd$battle_location: Cambodia (Kampuchea), Cambodia (Kampuchea), Thailand
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   427.0   524.8   622.5   622.5   720.2   818.0 
## -------------------------------------------------------- 
## bd$battle_location: Cambodia (Kampuchea), Thailand
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    29.0    80.5   132.0   132.0   183.5   235.0 
## -------------------------------------------------------- 
## bd$battle_location: Cameroon
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    32.0    44.0    56.0   266.7   384.0   712.0 
## -------------------------------------------------------- 
## bd$battle_location: Cameroon, Nigeria
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     189     236     283    1751    2532    4780 
## -------------------------------------------------------- 
## bd$battle_location: Central African Republic
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    26.0    35.0    45.0   106.4   139.5   325.0 
## -------------------------------------------------------- 
## bd$battle_location: Central African Republic, Chad
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     132     132     132     132     132     132 
## -------------------------------------------------------- 
## bd$battle_location: Central African Republic, DR Congo (Zaire), DR Congo (Zaire)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     100     291     482     482     673     864 
## -------------------------------------------------------- 
## bd$battle_location: Central African Republic, DR Congo (Zaire), South Sudan, Sudan, DR Congo (Zaire)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     136     136     136     136     136     136 
## -------------------------------------------------------- 
## bd$battle_location: Central African Republic, DR Congo (Zaire), Sudan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     238     238     238     238     238     238 
## -------------------------------------------------------- 
## bd$battle_location: Central African Republic, DR Congo (Zaire), Sudan, DR Congo (Zaire)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     120     120     120     120     120     120 
## -------------------------------------------------------- 
## bd$battle_location: Chad
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    34.0    97.0   161.0   360.7   394.0  1250.0 
## -------------------------------------------------------- 
## bd$battle_location: Chad, Chad, Sudan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1352    1352    1352    1352    1352    1352 
## -------------------------------------------------------- 
## bd$battle_location: Chad, Chad, Sudan, Sudan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     315     315     315     315     315     315 
## -------------------------------------------------------- 
## bd$battle_location: Chad, Niger, Nigeria
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    2667    2667    2667    2667    2667    2667 
## -------------------------------------------------------- 
## bd$battle_location: Chad, Sudan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      42      42      42      42      42      42 
## -------------------------------------------------------- 
## bd$battle_location: Chad, Sudan, Sudan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     353     353     353     353     353     353 
## -------------------------------------------------------- 
## bd$battle_location: China
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      36      36      36      36      36      36 
## -------------------------------------------------------- 
## bd$battle_location: China, Myanmar (Burma)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    80.0   137.2   194.5   194.5   251.8   309.0 
## -------------------------------------------------------- 
## bd$battle_location: Colombia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    30.0   217.8   642.5   730.1  1110.2  2263.0 
## -------------------------------------------------------- 
## bd$battle_location: Colombia, Colombia, Ecuador
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     244     244     244     244     244     244 
## -------------------------------------------------------- 
## bd$battle_location: Colombia, Venezuela
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     316     316     316     316     316     316 
## -------------------------------------------------------- 
## bd$battle_location: Comoros
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   29.00   35.75   42.50   42.50   49.25   56.00 
## -------------------------------------------------------- 
## bd$battle_location: Congo
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    51.0    53.0   167.0   838.8   651.0  3272.0 
## -------------------------------------------------------- 
## bd$battle_location: Congo, Congo, DR Congo (Zaire)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10033   10033   10033   10033   10033   10033 
## -------------------------------------------------------- 
## bd$battle_location: Croatia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     345     345     345     345     345     345 
## -------------------------------------------------------- 
## bd$battle_location: Djibouti
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   25.00   33.50   37.00   53.33   54.00  129.00 
## -------------------------------------------------------- 
## bd$battle_location: Djibouti, Somalia, Somalia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   379.0   961.8  1544.5  1544.5  2127.2  2710.0 
## -------------------------------------------------------- 
## bd$battle_location: DR Congo (Zaire)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    41.0   122.5   208.0   809.1   905.0  4457.0 
## -------------------------------------------------------- 
## bd$battle_location: DR Congo (Zaire), DR Congo (Zaire), Rwanda
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     703     872    1041    1041    1210    1379 
## -------------------------------------------------------- 
## bd$battle_location: DR Congo (Zaire), Rwanda
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    83.0   359.5   636.0   605.0   866.0  1096.0 
## -------------------------------------------------------- 
## bd$battle_location: DR Congo (Zaire), Sudan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      53      53      53      53      53      53 
## -------------------------------------------------------- 
## bd$battle_location: DR Congo (Zaire), Sudan, Uganda
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     221     221     221     221     221     221 
## -------------------------------------------------------- 
## bd$battle_location: DR Congo (Zaire), Uganda
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   182.0   225.2   268.5   268.5   311.8   355.0 
## -------------------------------------------------------- 
## bd$battle_location: DR Congo (Zaire), Uganda, Sudan, Uganda
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   189.0   413.2   637.5   637.5   861.8  1086.0 
## -------------------------------------------------------- 
## bd$battle_location: DR Congo (Zaire), Uganda, Uganda
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   388.0   610.5   833.0   820.3  1036.5  1240.0 
## -------------------------------------------------------- 
## bd$battle_location: Ecuador, Peru
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      99      99      99      99      99      99 
## -------------------------------------------------------- 
## bd$battle_location: Egypt
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   27.00   80.25  124.00  203.50  205.00  750.00 
## -------------------------------------------------------- 
## bd$battle_location: Egypt, Ethiopia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     191     191     191     191     191     191 
## -------------------------------------------------------- 
## bd$battle_location: El Salvador
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      88     468     848    1954    2886    4925 
## -------------------------------------------------------- 
## bd$battle_location: Eritrea
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    25.0    25.0    28.0    34.5    37.5    57.0 
## -------------------------------------------------------- 
## bd$battle_location: Eritrea, Ethiopia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1000   24096   47192   32731   48596   50000 
## -------------------------------------------------------- 
## bd$battle_location: Ethiopia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    25.0    25.0    25.0  1913.3    74.5 30633.0 
## -------------------------------------------------------- 
## bd$battle_location: Ethiopia, Ethiopia, Somalia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     211     211     211     211     211     211 
## -------------------------------------------------------- 
## bd$battle_location: Ethiopia, Kenya
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    25.0    25.0    25.0   189.5   189.5   683.0 
## -------------------------------------------------------- 
## bd$battle_location: Ethiopia, Kenya, Somalia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   940.0   993.8  1047.5  1047.5  1101.2  1155.0 
## -------------------------------------------------------- 
## bd$battle_location: Ethiopia, Somalia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    25.0    37.5    50.0    67.0    88.0   126.0 
## -------------------------------------------------------- 
## bd$battle_location: Ethiopia, Sudan, Sudan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1259    1259    1259    1259    1259    1259 
## -------------------------------------------------------- 
## bd$battle_location: France, Iraq, Libya, Syria
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   11557   11557   11557   11557   11557   11557 
## -------------------------------------------------------- 
## bd$battle_location: France, Israel, Lebanon
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      33      33      33      33      33      33 
## -------------------------------------------------------- 
## bd$battle_location: France, Sri Lanka
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    3868    3868    3868    3868    3868    3868 
## -------------------------------------------------------- 
## bd$battle_location: Georgia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   27.00   66.25  143.00  404.25  579.75 1611.00 
## -------------------------------------------------------- 
## bd$battle_location: Georgia, Russia (Soviet Union)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1289    1289    1289    1289    1289    1289 
## -------------------------------------------------------- 
## bd$battle_location: Germany, Netherlands, United Kingdom
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      44      44      44      44      44      44 
## -------------------------------------------------------- 
## bd$battle_location: Germany, United Kingdom
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      38      38      38      38      38      38 
## -------------------------------------------------------- 
## bd$battle_location: Guatemala
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   27.00   47.50   67.00   60.86   77.00   83.00 
## -------------------------------------------------------- 
## bd$battle_location: Guinea
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     217     217     217     217     217     217 
## -------------------------------------------------------- 
## bd$battle_location: Guinea-Bissau
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   199.0   275.5   352.0   352.0   428.5   505.0 
## -------------------------------------------------------- 
## bd$battle_location: Guinea-Bissau, Senegal
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   47.00   54.75   62.50   62.50   70.25   78.00 
## -------------------------------------------------------- 
## bd$battle_location: Guinea, Liberia, Sierra Leone
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     432     432     432     432     432     432 
## -------------------------------------------------------- 
## bd$battle_location: Haiti
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    48.0   104.0   160.0   138.3   183.5   207.0 
## -------------------------------------------------------- 
## bd$battle_location: India
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   25.00   35.25   76.00  252.89  264.25 2673.00 
## -------------------------------------------------------- 
## bd$battle_location: India, Myanmar (Burma)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      43      43      43      43      43      43 
## -------------------------------------------------------- 
## bd$battle_location: India, Pakistan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    25.0    29.5    68.5   462.2   198.2  2353.0 
## -------------------------------------------------------- 
## bd$battle_location: Indonesia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      26      51     187     268     286     915 
## -------------------------------------------------------- 
## bd$battle_location: Iran
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   28.00   30.25   52.50   66.50   88.75  133.00 
## -------------------------------------------------------- 
## bd$battle_location: Iran, Iran, Iraq
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   57.00   60.75   64.50   64.50   68.25   72.00 
## -------------------------------------------------------- 
## bd$battle_location: Iran, Iraq
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    27.0    32.0    42.5   102.1   116.8   356.0 
## -------------------------------------------------------- 
## bd$battle_location: Iran, Iraq, Iran, Pakistan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     121     121     121     121     121     121 
## -------------------------------------------------------- 
## bd$battle_location: Iran, Iraq, Italy, Pakistan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      76      76      76      76      76      76 
## -------------------------------------------------------- 
## bd$battle_location: Iraq
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      25      94     565    1094    1870    4094 
## -------------------------------------------------------- 
## bd$battle_location: Iraq, Kuwait, Philippines, Saudi Arabia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   21790   21790   21790   21790   21790   21790 
## -------------------------------------------------------- 
## bd$battle_location: Iraq, Lebanon, Syria
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   68503   68503   68503   68503   68503   68503 
## -------------------------------------------------------- 
## bd$battle_location: Iraq, Syria
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   13443   13443   13443   13443   13443   13443 
## -------------------------------------------------------- 
## bd$battle_location: Iraq, Syria, Turkey
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1214    1214    1214    1214    1214    1214 
## -------------------------------------------------------- 
## bd$battle_location: Iraq, Turkey
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      30     250     811    1306    1735    4183 
## -------------------------------------------------------- 
## bd$battle_location: Israel
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    27.0    60.0   120.0   330.3   459.5  1671.0 
## -------------------------------------------------------- 
## bd$battle_location: Israel, Israel, Lebanon
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     328     328     328     328     328     328 
## -------------------------------------------------------- 
## bd$battle_location: Israel, Israel, Syria
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     379     379     379     379     379     379 
## -------------------------------------------------------- 
## bd$battle_location: Israel, Lebanon
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    28.0    41.5    74.0   175.6   109.5   825.0 
## -------------------------------------------------------- 
## bd$battle_location: Israel, Malta
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      34      34      34      34      34      34 
## -------------------------------------------------------- 
## bd$battle_location: Ivory Coast
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    35.0    44.0    93.0   211.2   260.2   624.0 
## -------------------------------------------------------- 
## bd$battle_location: Ivory Coast, Liberia, Liberia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     347     347     347     347     347     347 
## -------------------------------------------------------- 
## bd$battle_location: Jordan, Syria
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      34    8517   17000   17000   25484   33967 
## -------------------------------------------------------- 
## bd$battle_location: Kenya, Somalia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    42.0   331.2  1362.0  1233.2  1840.0  2646.0 
## -------------------------------------------------------- 
## bd$battle_location: Kuwait
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1058    1058    1058    1058    1058    1058 
## -------------------------------------------------------- 
## bd$battle_location: Kyrgyzstan, Tajikistan, Uzbekistan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      67      67      67      67      67      67 
## -------------------------------------------------------- 
## bd$battle_location: Kyrgyzstan, Uzbekistan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     471     471     471     471     471     471 
## -------------------------------------------------------- 
## bd$battle_location: Laos
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   30.00   84.75  139.50  139.50  194.25  249.00 
## -------------------------------------------------------- 
## bd$battle_location: Lebanon
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    28.0    45.0    79.0   194.2   115.0   711.0 
## -------------------------------------------------------- 
## bd$battle_location: Lebanon, Syria
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   54547   54547   54547   54547   54547   54547 
## -------------------------------------------------------- 
## bd$battle_location: Lesotho
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      68      68      68      68      68      68 
## -------------------------------------------------------- 
## bd$battle_location: Liberia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    27.0    57.0   341.0   540.8   492.0  1787.0 
## -------------------------------------------------------- 
## bd$battle_location: Liberia, Sierra Leone
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     415     415     415     415     415     415 
## -------------------------------------------------------- 
## bd$battle_location: Libya
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   118.0   156.0   322.0   840.8  1678.0  1930.0 
## -------------------------------------------------------- 
## bd$battle_location: Macedonia, FYR, Serbia (Yugoslavia)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      72      72      72      72      72      72 
## -------------------------------------------------------- 
## bd$battle_location: Malaysia, Philippines
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      70      70      70      70      70      70 
## -------------------------------------------------------- 
## bd$battle_location: Mali
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   34.00   44.25   90.50   87.00  103.50  203.00 
## -------------------------------------------------------- 
## bd$battle_location: Mali, Mali, Niger
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     839     839     839     839     839     839 
## -------------------------------------------------------- 
## bd$battle_location: Mali, Mauritania
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   26.00   26.75   27.50   27.50   28.25   29.00 
## -------------------------------------------------------- 
## bd$battle_location: Mexico
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      37      64      91      91     118     145 
## -------------------------------------------------------- 
## bd$battle_location: Moldova
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     585     585     585     585     585     585 
## -------------------------------------------------------- 
## bd$battle_location: Morocco
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     207     207     207     207     207     207 
## -------------------------------------------------------- 
## bd$battle_location: Mozambique
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    26.0    97.5   729.0   735.7  1281.8  1577.0 
## -------------------------------------------------------- 
## bd$battle_location: Myanmar (Burma)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    25.0    30.5    68.0   125.3   126.2  1163.0 
## -------------------------------------------------------- 
## bd$battle_location: Myanmar (Burma), Myanmar (Burma), Thailand
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   114.0   115.8   117.5   117.5   119.2   121.0 
## -------------------------------------------------------- 
## bd$battle_location: Myanmar (Burma), Thailand
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    33.0    35.5   117.0   256.9   288.5  1000.0 
## -------------------------------------------------------- 
## bd$battle_location: Nepal
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    40.0   203.0   429.0   901.5  1222.5  3947.0 
## -------------------------------------------------------- 
## bd$battle_location: Nicaragua
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    39.0   160.8   282.5   282.5   404.2   526.0 
## -------------------------------------------------------- 
## bd$battle_location: Niger
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   29.00   40.00   50.00   87.56   81.00  274.00 
## -------------------------------------------------------- 
## bd$battle_location: Nigeria
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    61.0   177.0   405.0   845.1  1629.0  2213.0 
## -------------------------------------------------------- 
## bd$battle_location: Pakistan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    38.0    64.0    89.5  1028.1   871.0  6303.0 
## -------------------------------------------------------- 
## bd$battle_location: Panama
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    77.0   268.5   460.0   460.0   651.5   843.0 
## -------------------------------------------------------- 
## bd$battle_location: Papua New Guinea
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    26.0    29.0    34.0    44.4    55.0    78.0 
## -------------------------------------------------------- 
## bd$battle_location: Papua New Guinea, Solomon Islands
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      35      35      35      35      35      35 
## -------------------------------------------------------- 
## bd$battle_location: Paraguay
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     150     150     150     150     150     150 
## -------------------------------------------------------- 
## bd$battle_location: Peru
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    25.0    45.0    93.0   441.7   854.0  1827.0 
## -------------------------------------------------------- 
## bd$battle_location: Philippines
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    32.0   114.5   193.0   299.9   313.0  1514.0 
## -------------------------------------------------------- 
## bd$battle_location: Qatar, Russia (Soviet Union)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1151    1151    1151    1151    1151    1151 
## -------------------------------------------------------- 
## bd$battle_location: Rumania
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     249     249     249     249     249     249 
## -------------------------------------------------------- 
## bd$battle_location: Russia (Soviet Union)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    30.0   144.5   290.0   793.4   675.0  5769.0 
## -------------------------------------------------------- 
## bd$battle_location: Russia (Soviet Union), Syria
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      30      30      30      30      30      30 
## -------------------------------------------------------- 
## bd$battle_location: Russia (Soviet Union), Turkey
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     359     359     359     359     359     359 
## -------------------------------------------------------- 
## bd$battle_location: Russia (Soviet Union), Ukraine
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     712     712     712     712     712     712 
## -------------------------------------------------------- 
## bd$battle_location: Rwanda
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    87.0   121.5   353.0   701.0  1090.0  2044.0 
## -------------------------------------------------------- 
## bd$battle_location: Saudi Arabia, Yemen (North Yemen)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    2536    2536    2536    2536    2536    2536 
## -------------------------------------------------------- 
## bd$battle_location: Saudi Arabia, Yemen (North Yemen), Yemen (North Yemen)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    6532    6532    6532    6532    6532    6532 
## -------------------------------------------------------- 
## bd$battle_location: Senegal
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    25.0    37.5   145.5   156.0   256.2   319.0 
## -------------------------------------------------------- 
## bd$battle_location: Serbia (Yugoslavia)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    64.0   100.0   673.5  1092.2  1665.8  2958.0 
## -------------------------------------------------------- 
## bd$battle_location: Sierra Leone
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    48.0   424.5   599.0  1105.8  1291.0  3287.0 
## -------------------------------------------------------- 
## bd$battle_location: Somalia
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      25     228     547    1362    1491    8005 
## -------------------------------------------------------- 
## bd$battle_location: South Sudan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   137.0   285.2   613.0   695.3   877.8  1667.0 
## -------------------------------------------------------- 
## bd$battle_location: South Sudan, Sudan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     367     367     367     367     367     367 
## -------------------------------------------------------- 
## bd$battle_location: South Sudan, Sudan, Sudan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     856     958    1060    1060    1162    1264 
## -------------------------------------------------------- 
## bd$battle_location: Spain
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      44      44      44      44      44      44 
## -------------------------------------------------------- 
## bd$battle_location: Sri Lanka
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    29.0   589.8  2284.5  2868.3  3844.2 10165.0 
## -------------------------------------------------------- 
## bd$battle_location: Sudan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     145     684    1128    1610    2222    4891 
## -------------------------------------------------------- 
## bd$battle_location: Sudan, Uganda
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   295.0   668.5  1317.0  1878.1  2931.5  4335.0 
## -------------------------------------------------------- 
## bd$battle_location: Sudan, Uganda, Uganda
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     888     888     888     888     888     888 
## -------------------------------------------------------- 
## bd$battle_location: Syria
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      25      56     964    6052   11161   27190 
## -------------------------------------------------------- 
## bd$battle_location: Syria, Turkey
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      26    9640   19253   19253   28866   38480 
## -------------------------------------------------------- 
## bd$battle_location: Tajikistan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   28.00   76.75  227.00  922.62 1147.50 3376.00 
## -------------------------------------------------------- 
## bd$battle_location: Thailand
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   26.00   69.75  134.50  127.14  182.75  212.00 
## -------------------------------------------------------- 
## bd$battle_location: Trinidad and Tobago
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      39      39      39      39      39      39 
## -------------------------------------------------------- 
## bd$battle_location: Turkey
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   29.00   77.25  218.50  352.67  310.50 2117.00 
## -------------------------------------------------------- 
## bd$battle_location: Uganda
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    72.0   105.5   385.0   467.5   795.0  1019.0 
## -------------------------------------------------------- 
## bd$battle_location: Ukraine
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      88     255    1304    1040    1558    1996 
## -------------------------------------------------------- 
## bd$battle_location: United Kingdom
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   26.00   26.75   27.50   27.50   28.25   29.00 
## -------------------------------------------------------- 
## bd$battle_location: Uzbekistan
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##      37      37      37      37      37      37 
## -------------------------------------------------------- 
## bd$battle_location: Venezuela
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     144     144     144     144     144     144 
## -------------------------------------------------------- 
## bd$battle_location: Yemen (North Yemen)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    94.0   228.2   861.0   964.5  1531.8  2330.0

I also wanted to see a finer breakdown of the data by territory, so I printed the summary. I also attempted a bar plot of the battle deaths per battle location. The codebook explicitly calls out against using battle locations for geospatial analysis, but I only wanted to see which locations have the most battles.

Reflections: Univariate

Although this section has only been devoted to analyzing single variables at a time, we have already learned quite a bit. We have seen regional differences in conflict and how bloody some types of conflict are compared to others. We will continue by comparing two variables agaisnt each other for a deeper understanding of global violence.

Univariate Analysis

What is the structure of your dataset?

I will repeat here what I wrote above when explaining the structure of the data set:

The dataset has an accompanying codebook that illuminates the more confusing variables.

There were two datasets, one that was a “Dyadic” set, a collection of two-party battles, and the set I used which was a conflict set. The Dyadic ID was carried over from the dyadic set.

Incompatibility returns 1 or 2. 1 stands for ‘incompatibility about government’ indicating that the source of the conflict is a discrepency over governmental affairs. 2 stands for ‘incompatibility about territory’ indicating a resource based conflict.

Another prominent variable will be the type_of_conflict variable. It is also given a numeric evaluation from 1 to 4. 1 stands for extrasystemic - I will be honest, the codebook is lacking for explanatinon as to what this could mean. 2 stands for interstate, meaning two or more countries at war with each other. 3 stands for internal, so mostly civil war or revolutions that do not involve other state entities. 4 stands for international internal, which would refer to a conflict in which a state is deal with internal unrest but has also accepted support from foreign troops.

What is/are the main feature(s) of interest in your dataset?

I want to see if the conflicts and battle related deaths have decreaseed or increased over time, as was studied by Steven Pinker. Additionally, if these death increased/decreased across all categories uniformly or whether they decreased or increased in certain types of conflict only.

What other features in the dataset do you think will help support your
investigation into your feature(s) of interest?

The regions may be of interest. Particularly with the incompatibility about territory types of conflict, it would unveil which regions are most prone to resource based conflicts. It would’ve been interesting to compare with population data also, but that may be foregone.

Did you create any new variables from existing variables in the dataset?

No. Pinker has claimed that he used as raw and unfiltered data as possible and I strive to do the same.

Of the features you investigated, were there any unusual distributions?
Did you perform any operations on the data to tidy, adjust, or change the form
of the data? If so, why did you do this?

Yes. There was a 3 given for incompatibility, which should not be there according to the codebook, so that too was dropped. One unusual distribution was the rise in battle deaths count for international internal, which I believe has to do with the Syrian conflict.

Bivariate Plots Section

At first, I just wanted to see battle deaths by year for each battle. This might’ve been better placed in the univariate section, but I also overlayed a max trendline. I plotted one with raw data, one with a square root axis, and one with a log axis. For all except the log axis plot I plotted the line for the maximum battle deaths. For the log plot I plotted the mean line for battle deaths. This sets up what to expect ot of the next series of plots.

Following from the previosu plots, I also wanted to parse out battle deaths by type of conflict (the first plot), incompatibility (the second) and region (the third plot). We saw a rise in type three and type 4 conflicts, civil wars and internationalized civil wars, which I attribute by timeline ot be the syrian conflict. By incompatibility we also see a start rise in incompatibility over resources (2). And of course, when we look at battle deaths by region we can see a sharp spike in region 2, the middle east.

I thought it might be best to use box plots to get a better understanding of the data. We could see with most of the above plots that there were few high casualty battle deaths but many low casualty deaths, which would make a box plot a better choice. I fumbled for a scale, but the log scale seems like the best one. I started, at random, by looking at a box plot for battle deaths by region. Region 2, the middle east, had much higher outliers but the medians seemed to be on par with other regions. In fact, the only region with a lower median was region 3, Asia, althought they had a higher frequency of outliers.

I did a similar thing for incompatibility and battle deaths. As we’ve seen a few times now, incompatibility over territory tends to draw more battle deaths than incompatibility over government.

## # A tibble: 6 x 4
##    year bd_best_mean bd_best_med     n
##   <int>        <dbl>       <dbl> <int>
## 1  1989        1358.        244.    40
## 2  1990        1626.        203     49
## 3  1991        1351.        156.    52
## 4  1992         744.        165     49
## 5  1993         856.        154     43
## 6  1994         693.        147     48

For this section I grouped the battle deaths by year and cycled through different axises. The mean battle deaths by year with a mean trendline is important, it shows a surprising slight cyclical trend in battle deaths. Its clear that by raw numbers there have been higher battle deaths in the past few years than previously. It also looks like high-casualty battles happen every ten to twelve years (but this maybe the relatively small timeframe over which the data is collected.)

I took the same data set grouped by year and filtered for type of conflict. We’ve already seen to some extent how much more prevalent type 3, civil conflict, and type 4, internationalized civil conflict, is but this plot brought home exactly how much more prevalent civil conflicts are as opposed to interstate conflicts. This is surprising to me because I tend to think of battle deaths as between two or more nations, but it appears that it is more common for battle deaths to occur within states. While interstate conflict and battle deaths are very devastating when they happen, intrastate conflict is much more prevalent.

Looking at battle deaths by year by incompatibility, we can see that type 1 incompatibilty, incompatibility over government, seemed to peak in the late 1990s and by 2000. Conversely, battle deaths by type 2 incompatibilty (over territory) saw a swell at around 2013-2014. Overall though, incompatibilites about government seem to on the whole be declining even though the most prevalent type of conflict, as we saw earlier, was intrastate. This is strange, I would expect most intrastate conflict to be about government/how things are run but it may be pointing to how states are not providing enough territory or resources for their people.

Looking at the log graph again, we seem some familiar ideas playing out. Region 2, the middle east, has a tumultuous ascendency that’s peaked in recent years. That was expected. Region three, Asia, has high frequencies of battle deaths that is probably due to the hgih population and size of the continent. The average of battle deaths in Asia is on par with mos tof the other regions. The surprising region was region 5, the Americas, which has a noticable downward trend.

Reflection: Bivariates

Many of the observations made in the univariate section were expanded upon in this section. For example, we knew from the unvariate analysis that type two incompatibility was responsible for a higher battle death count than type one, but we could see specific peaks and valleys. It seemed like government incompatibilities, while devastting when they occured, were declining compared to territory incompatibilities. Insights like this were made through out this section.

The analysis itself was more nuanced in this section. Many times, the same data had to be plotted against different axises in order to see clear trends or better evaluate the data.

Bivariate Analysis

Talk about some of the relationships you observed in this part of the
investigation. How did the feature(s) of interest vary with other features in
the dataset?

The absolute number of battle deaths seems to have gone up, but does so periodically. I was very surprised at the seeming sinusoidal behavior of it.

Did you observe any interesting relationships between the other features
(not the main feature(s) of interest)?

By incompatibility type we see that conflict with/because of government had a spike in 2000 but conflict due to territory increased around 2015. Looking at conflict type, we see a similar spike for 2 - interstate at around 2000 and a slight bump for conflict type 3 - internal. Not that internal has a low average but is of much higher frequency.

What was the strongest relationship you found?

By type of cnflict, 3 - internal conflict is incredibly frequent. It doesn’t show as much in terms of averages because the casualties are low but they occur far, far more than other data.

Multivariate Plots Section

## # A tibble: 6 x 5
## # Groups:   year [2]
##    year region mean_bd_best2 median_bd_best2t     n
##   <int> <fct>          <dbl>            <dbl> <int>
## 1  1989 1              144.              144.     2
## 2  1989 2              260.              140.     4
## 3  1989 3              747.              194     13
## 4  1989 4             2879.              699     12
## 5  1989 5              968.              238      9
## 6  1990 1               78.7              49      3

The first multivariate graph was plotting the multiple regions, by battle death by year, on one graph. The first graph was using the means of battle deaths and showed about what I expected given the bivariate analysis. There was, as expected, a sharp rise in recent years in region 2, the middle east. There were shapr bumps in region four, Africa, and region one, Europe. THe second grpah, by medians, was very surprising however. This showed that the late 90s and early 00s had the highest median battle deaths in region one, Europe. This might’ve been the Bosnian War or possibly the Irish Peace Process. Either way, this was incredibly surprising since it did not show up in any of the previous analyses.

## # A tibble: 6 x 5
## # Groups:   year [3]
##    year incompatibility mean_bd_best2 median_bd_best2t     n
##   <int>           <int>         <dbl>            <dbl> <int>
## 1  1989               1          601.              115    15
## 2  1989               2         1812.              526    25
## 3  1990               1         1517.               76    26
## 4  1990               2         1750.              700    23
## 5  1991               1         1794               100    25
## 6  1991               2          940.              220    27

Showing both forms of incompatibility on the smae graph reiterates exactly the sentiments I believed earlier. Type 2 incompatibility, over territory, is far more prevalent and more responsible for a higher battle dath count than type one. The first graph shows how conflicts over territory have increased to a startling amount in recent years. THe second graph shows the median mbattle deaths by incompatibility and shows that the median deaths are always higher except for one year in 1993.

## # A tibble: 6 x 6
## # Groups:   year [2]
##    year type_of_conflict sum_bd_best2 mean_bd_best2 median_bd_best2t     n
##   <int> <fct>                   <int>         <dbl>            <dbl> <int>
## 1  1989 2                         868          434              434      2
## 2  1989 3                       42775         1296.             207     33
## 3  1989 4                       10662         2132.            1149      5
## 4  1990 2                        1083          542.             542.     2
## 5  1990 3                       75195         1709.             142     44
## 6  1990 4                        3403         1134.            1126      3

Analyzing by type of conflict shows that, as has been a recurring observation, type 3 conflicts and type four conflicts have been steadily rising globally. This is most prevalent in thefirst graph, which orders by sums. When we look by medians, we can see spiked at around 1990 and 2000 and see that the median battle deaths are highest for type 2, interstate conflicts. Same is true for the plot by mean. Another interesting trend I noticed was the sharp rise at around 2013 in conflicts that were categorized as both civil and internationalized civil.

Reflections: Multivariates

Much of the analysis performed her was a continuation of the anlysis performed in the bivariate section, but was clenaer and produced more insightful graphs and visualizations. It is easier to compare trends against each other on the same plot and not across plots, so that intrinsically revealed some interesting insights.

Multivariate Analysis

Talk about some of the relationships you observed in this part of the
investigation. Were there features that strengthened each other in terms of
looking at your feature(s) of interest?

Type of conflict, when plotted directly against other types of conflict still showed that 2 - inter state conflict caused the most battle deaths at the year 2000. This supports an earlier observation. I also saw a rise in 4 - international internal conflicts and on the region graph a massive spike in region 2 - the middle east after 2010, somewhat supporting my hypothesis of Syria’s effect on the data.

Were there any interesting or surprising interactions between features?

Because of the previous observation of the frequency of type 3 conflicts - internal, I summed by conflict type. Interestingly, it seems as though internal conflicts (3) cycle off with state conflicts (2). Also, as international internal deaths rose, there was a steep drop off in type 3 - internal deaths. Was this a change in how the data was categorized?

OPTIONAL: Did you create any models with your dataset? Discuss the
strengths and limitations of your model.

No, but the cyclical nature seems to indicate a model might exist.


Final Plots and Summary

Plot One

Description One

THe plots show the number of battle deaths by conflict type, where each conflict type was described in the accompanyng codebook as: (1) - extrasystemic, (2) Interstate, (3) Internal (involving no foreign agents) and (4) internationalized internal (internal conflicts with interstate forces). These plots show a few thing well, namely the frequency of internal conflicts and the intermittency of interstate conflicts. While the average for internal conflicts is low its frequency overshadows other types of conflict.

Plot Two

Description Two

These plots show battle deaths by region, where the codebook again reveals that (1) Europe, (2) is the Middle East, (3) is Asia, (4) is Africa and (5) is the Americas. This shows, interestingly, that the violence in the Middle East is of late unprecedented. Also this shows, though hard to see, a slow but steady growth of battle deaths in Asia. This also shows how different violence is distributed regionally.

Plot Three

Description Three

I wanted to show a simple graph, as Pinker would show, of just the average battle deaths per year against the year. It shows, most interestingly a weak sort of sine wave. While alot of these are explained by violence in the middle east or the tragedy of Darfur, its seeming periodicity is striking.


Reflection

Pinker did do one thing to his datasets that I simply was unable to do and that was he made all data shown as per capita. I could not find a compatible enough dataset without truncating too much data, but this is one area for future study. My biggest criticism of Pinker’s analysis has been how he does not compare battle deaths to death rates per population, so this is another area I would like to study.

All in all, I was surprised by the periodicity of the data sometimes. It might’ve been naive analysis, but they do say history repeats itself, so it would make sense that it shows up in the data.